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Why AI Projects Should Be Built Like Supply Chains

Published

3 mois ago

14 juillet 2026

An enterprise team begins an artificial intelligence project with a familiar question: Which model should we use? The team compares benchmarks, studies pricing, debates whether the newest large model is worth the additional cost, and eventually selects a platform.

The first outputs arrive, and some are impressive. Others contain unsupported claims, repeat information from earlier sections, ignore important instructions, or vary sharply in quality from one run to the next.

The team responds by expanding the prompt and adding more context. Before long, the model is being asked to research the subject, verify facts, organize findings, write the report, match the company’s voice, improve readability, optimize the structure, check for duplication, and ensure that nothing important is missing.

The prompt grows longer, but the underlying problem remains. The project has been designed around a single model interaction even though the work itself is not a single task.

It is a process, and supply chain professionals should recognize the mistake immediately. No modern manufacturer expects one machine to receive raw materials, fabricate every component, assemble the finished product, inspect it, package it, and prepare it for delivery.

Complex production is divided into specialized stages because each stage requires different capabilities, controls, and measures of quality. Enterprise AI is moving toward the same realization.

The future of AI will not be defined only by larger models. It will be defined by better production systems.

From a Prompt to a Production Line

Imagine that the enterprise team starts again, but this time it does not ask one model to produce the finished report from a blank prompt. Instead, the work is divided into a sequence of specialized stages.

The first stage collects source material. The second extracts relevant facts and records where they came from.

The third stage classifies those facts and identifies relationships between them. The fourth builds a structured outline that reflects the intended audience, argument, and format.

A drafting model then turns that outline into prose. A stronger editorial model reviews the completed draft for clarity, consistency, factual alignment, and tone.

Finally, deterministic software checks measurable requirements such as word count, metadata, links, formatting, duplicate language, and required sections. The result is not simply another prompt but an AI production line.

Each stage has a defined input and output, and each handoff can be inspected before the work continues downstream. That changes the nature of the system.

When an output is weak, the team no longer has to guess what went wrong. It can determine whether the problem originated in research, classification, outlining, drafting, editing, or quality assurance.

A missing fact can be traced back to retrieval, while a weak structure can be corrected in the outline. Repetitive writing can be addressed during editorial review, and a broken link can be fixed during quality assurance without regenerating the entire document.

The system becomes diagnosable, which is one of the defining differences between an experiment and an industrial process. Once defects can be traced to specific stages, they can be corrected systematically rather than treated as random model behavior.

The Lesson Supply Chains Already Learned

Supply chains became more capable by abandoning the idea that one facility, one supplier, or one process should do everything. Specialization improved performance, while standardization improved handoffs.

Quality gates prevented defects from moving downstream, and visibility made it possible to identify bottlenecks. Redundancy reduced dependence on a single point of failure, while continuous improvement raised performance over time.

The same principles increasingly apply to AI. A low-cost model may be well suited to extracting structured facts from a set of documents, while a more capable model may be better at synthesis and editorial judgment.

Python or another deterministic tool may be more reliable for validation, deduplication, calculations, and file handling. A knowledge graph may be better than a language model at preserving relationships between entities.

A human expert may remain essential when the decision involves ambiguity, risk, or strategic judgment. The objective is not to force one model to perform every task but to orchestrate the best combination of models, software, data, and human expertise.

That is fundamentally a supply chain problem. It involves routing work through the right sequence of specialized resources to produce a reliable outcome.

Cost Savings Are Only the Beginning

The immediate financial argument for this approach is easy to understand. Organizations do not need to use their most expensive model for every step.

Routine extraction, classification, and formatting can often be handled by smaller models or deterministic software. More capable models can then be reserved for the moments where reasoning, synthesis, or editorial judgment create the greatest value.

That can substantially reduce token costs, but cost reduction is not the most important benefit. The larger advantage is control.

A one-step system asks a model to interpret a broad objective and quietly make hundreds of intermediate decisions. Those decisions are usually invisible to the user.

When the final answer is wrong, inconsistent, or incomplete, there may be no clear way to determine why. A multi-step system exposes those decisions and makes them easier to inspect.

Research can be reviewed before drafting begins, classifications can be tested against a taxonomy, and claims can be linked to sources. Drafts can be compared with the underlying evidence, while quality rules can be applied consistently across every output.

The organization is no longer merely generating content. It is managing a production process.

The Value of the Work in Progress

In traditional manufacturing, work in progress is usually viewed as inventory that must be controlled. In AI production, the intermediate work can become an asset of its own.

Consider a company building a supplier directory. A one-step workflow might ask a model to visit a supplier’s website and write a profile.

When the profile is complete, the underlying research effectively disappears inside the finished prose. The final page may be useful, but the evidence and structure used to create it are difficult to reuse.

A multi-step workflow would first create a structured supplier packet containing the company’s canonical name, capabilities, products, industries served, geographic presence, source links, category assignments, confidence levels, and unresolved questions. That packet can support the supplier profile, but it can also support much more.

It can populate a comparison table, connect the supplier to an industry ontology, support a market map, feed a research report, improve internal linking, and provide context to an AI assistant. It can also be updated later without repeating the entire research process.

The article or profile becomes one expression of the knowledge rather than the knowledge itself. This is an important distinction because the most valuable output of an AI system may not be the document that appears on the screen.

The more durable asset may be the structured knowledge created along the way. That knowledge can support multiple products, channels, and future workflows.

Quality Cannot Be Added at the End

Many enterprises still treat quality control as a final review step. The model produces an answer, and a human is asked to check it.

That approach does not scale well. A reviewer examining one report can catch obvious problems, but a reviewer responsible for hundreds or thousands of pages cannot reconstruct every source, validate every classification, compare every phrasing pattern, and confirm every piece of metadata.

Quality must therefore be designed into the process. That means validating source quality before information enters the system and distinguishing verified facts from model inference.

It also means using structured schemas so required fields cannot quietly disappear. Confidence thresholds should be applied, and uncertain cases should be escalated for human review instead of being treated as equally reliable.

Not every stage should be handled by generative AI. Language models are strong at interpretation, synthesis, and expression, but they are less dependable when exact counting, deterministic comparison, or strict rule enforcement is required.

A mature architecture uses generative AI where judgment is needed and conventional software where certainty is required. The same principle applies in supply chains, where inspection cannot compensate for a production system that repeatedly introduces defects.

Quality must be built upstream. The earlier a defect is found, the less damage it can cause downstream.

Visibility Changes Management

For decades, supply chain leaders have invested in visibility because they understand that an organization cannot manage what it cannot see. The same is true for AI workflows.

A black-box interaction provides very little operational visibility. A request goes in, an answer comes out, and the intermediate work remains hidden.

A staged system can reveal which sources were retrieved, which facts were accepted, and which claims had low confidence. It can also show which model handled each stage, where the final reviewer made changes, and which outputs repeatedly failed quality checks.

This creates the possibility of managing AI performance rather than merely observing it. Over time, the organization can identify recurring defects, improve prompts, replace weak models, update templates, refine taxonomies, and strengthen source selection.

The AI system begins to improve not only through model upgrades but through process improvement. That distinction is important because an enterprise can strengthen the overall system even when the underlying models remain unchanged.

Resilience Matters in AI Too

Dependence on one model also creates a form of concentration risk. A model may become more expensive, its behavior may change, its context limits may become restrictive, or its availability may decline.

A new model may outperform it in one task but not another. Organizations that build their entire workflow around one provider or one model may therefore find it difficult to adapt.

A modular pipeline is more resilient. The extraction model can be replaced without redesigning the editorial stage, and the review model can be upgraded without rebuilding the research process.

A vector database can be changed while preserving the output schema. A human approval step can also be added to a high-risk workflow without disrupting the rest of the architecture.

This modularity resembles a well-designed supply network in which components can change while the broader system continues to operate. The objective is not merely efficiency but adaptability.

The Model Is Not the System

The first phase of enterprise AI encouraged organizations to think of the model as the product. That was understandable because the model was the most visible and impressive component.

A model alone, however, is no more a complete enterprise system than an engine is a complete transportation network. The value comes from what surrounds it.

That includes the data entering the system, the context provided to the model, the tools it can call, and the rules governing its behavior. It also includes the mechanisms that validate its output, the people who supervise important decisions, the knowledge retained after the task is complete, and the feedback used to improve future performance.

This is why the question “Which model should we use?” is becoming less useful on its own. A better question is, “How should the work move through the system?”

That question forces the enterprise to think about architecture, handoffs, quality, governance, and continuous improvement. It shifts the conversation from model selection to operating design.

Building Intelligence as a Supply Chain

The analogy is not rhetorical because it offers a practical blueprint. Raw information enters the system in much the same way raw material enters a production network.

Retrieval and extraction prepare it for use, while classification and ontology assign meaning and structure. Planning organizes the work into a viable production sequence.

Generation transforms the structured inputs into a usable product, while editorial review improves the finish. Quality assurance checks conformity, and human experts manage exceptions.

Performance data then flows back upstream to improve the next production cycle. Seen this way, AI is not simply answering questions.

It is converting information into decisions, documents, recommendations, and knowledge assets through a coordinated sequence of transformations. That is precisely what supply chains do with physical goods.

The Next Competitive Advantage

Access to powerful models will continue to broaden, and the models themselves will continue to improve. Prices will change, benchmark leaders will rotate, and new providers will emerge.

As that happens, access to a particular model will become less of a durable advantage. The more defensible capability will be the system built around the models.

Organizations that develop reliable research packets, proprietary taxonomies, structured knowledge bases, quality-control rules, reusable workflows, and feedback loops will be able to produce stronger results regardless of which model happens to lead the market at a given moment. Their advantage will come from orchestration rather than access.

They will know how to route work, which tasks require premium reasoning, and which do not. They will also know where human judgment creates value and how to preserve knowledge instead of discarding it after every interaction.

They will improve the process with every production cycle. That is the larger lesson.

The future of enterprise AI will not belong to the companies that simply buy access to the largest model. It will belong to the companies that build the best intelligence supply chains.

Those companies will treat information as an input, knowledge as an asset, quality as a process, and AI as a coordinated production system rather than a single prompt. Supply chain leaders have spent decades learning how to design systems that are specialized, visible, resilient, and continuously improving.

Those same principles may now become some of the most important principles in enterprise AI.

The post Why AI Projects Should Be Built Like Supply Chains appeared first on Logistics Viewpoints.

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From Prompting to Persistent Decision Workflows in Supply Chain Operations

Published

10 heures ago

13 octobre 2026

Supply-chain AI is moving from episodic prompting toward persistent decision workflows. Repeated prompting can help an analyst solve a problem, but an operating capability must maintain context, monitor changing state, preserve decision logic, and connect recommendations to the work that follows.

A persistent decision workflow needs memory of the business object, the current plan, previous recommendations, overrides, policies, and outcomes. Without that continuity, users repeatedly reconstruct context and the model cannot reliably distinguish a new exception from a continuation of an old one. This persistence requirement becomes more important as the AI-agent execution model advances, because an agent cannot act safely if each interaction starts without durable operating state or prior decision context.

A mature design connects detection, context assembly, analysis, option generation, recommendation, approval, execution, and feedback. Some stages may use generative AI; others are better handled by optimization, rules, deterministic integrations, or human judgment. Persistence comes from the workflow and state model, not from the conversational interface.

The system should know what it may observe, recommend, prepare, execute, and escalate. Financial thresholds, customer impact, reversibility, confidence, and policy can determine the autonomy level. These controls make repeated decisions governable at scale.

ERP, planning, TMS, WMS, visibility, risk, and network platforms remain systems of record and execution; decision intelligence sits above and across them where context is assembled, tradeoffs are evaluated, actions are prioritized, and responses are coordinated. The persistent workflow therefore needs controlled write-back or action mechanisms, not only read access and summarization.

A decision workflow should record recommendations, human overrides, actions, and results. That creates an evidence base for improving rules, models, prompts, and autonomy thresholds. A chat transcript alone is not an operating learning system.

The architectural shift is from asking AI for help to designing a governed process in which intelligence participates continuously. That is a much stronger foundation for supply-chain decision support than repeated prompting.

Persistent decision workflows need memory and governance

A durable decision workflow has to preserve operating state across time. It should retain the event, relevant context, alternatives, constraints, prior decisions, approvals, overrides, actions, and outcomes so that the next step does not depend on a user reconstructing the problem in a new prompt. That persistence is what turns AI assistance into an operational capability.

Buyers should test continuity across handoffs. Can the system resume an unresolved decision after new data arrive? Can a different user see why an option was rejected? Can an agent act only within encoded authority? Can the resulting transaction be traced back to the evidence and rule that produced it? These are architecture questions, not user-interface questions.

Persistence changes the economics of decision support

When a workflow is persistent, the organization stops paying the same context-assembly cost every time a decision changes hands. New events can be evaluated against the existing state, prior alternatives remain visible, and approvals or overrides do not have to be reconstructed from email or chat history. That can reduce decision latency while improving traceability. It also creates a better foundation for automation because the system can distinguish a new decision from a continuation of an existing one. Buyers should therefore evaluate memory, state management, versioning, and auditability as operating capabilities rather than treating them as secondary AI features.

The 2026 Market Map is designed to help organizations understand the structure of the Decision Intelligence market, evaluate provider differences, and identify the capabilities most relevant to their operating environment. If your organization is evaluating Decision Intelligence platforms or clarifying where decision intelligence fits within the broader technology architecture, I would be glad to provide the Market Map brochure and discuss the evaluation questions and provider differences most relevant to your requirements.

Request the Decision Intelligence Market Map Brochure

For technology providers

Providers may request the brochure, discuss the research framework, or contact me to confirm how their capabilities are represented in the market assessment.

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Why SKU Selection Can Make or Break an AS/RS Business Case

Published

12 heures ago

13 octobre 2026

Why Sku Selection Can Make Or Break An As/rs Business Case

Automated Storage and Retrieval Systems (AS/RS) have become a cornerstone of modern warehouse automation strategies. Whether the technology involves shuttles, cube-based systems, cranes, or goods-to-person solutions, the business case is typically built around a common set of objectives: increasing throughput, reducing labor requirements, improving accuracy, and maximizing storage utilization. Yet one of the most important decisions in any AS/RS implementation often receives far less attention than the technology itself: SKU selection.

Companies frequently spend months evaluating vendors, software platforms, and automation architectures. However, even the most advanced AS/RS can fail to deliver expected returns if the wrong products are placed into the system. In many cases, the difference between a highly successful deployment and a disappointing investment comes down to a simple question: Which SKUs should be automated?

The Purpose of AS/RS Is Productivity, Not Storage

One of the most common misconceptions about AS/RS is that its primary purpose is to store inventory. While increased storage density is certainly a benefit, the real value of automation lies in improving warehouse productivity.

AS/RS creates value by reducing manual labor associated with inventory handling. Every automated retrieval eliminates walking, searching, traveling, and material movement. The more frequently a product is handled, the greater the opportunity for automation to generate measurable savings.

This is where SKU velocity becomes critical.

Not all inventory contributes equally to warehouse activity. In most distribution centers, a relatively small percentage of SKUs generates a disproportionately large percentage of order lines, picks, and replenishment activity. These high-velocity items create the majority of warehouse work and therefore present the greatest opportunity for automation.

Why High-Velocity SKUs Belong in AS/RS

The strongest AS/RS business cases are generally built around automating the products that move most frequently.

Consider two products:

SKU A is picked 2,000 times per week.

SKU B is picked 10 times per month.

Both consume storage space within the AS/RS, but only one generates meaningful operational value.

Every time SKU A is automatically retrieved, labor is eliminated and productivity improves. Over the course of a year, those savings accumulate rapidly. SKU B, on the other hand, occupies valuable automated storage capacity while contributing very little to throughput improvement or labor reduction.

Simply put, high-velocity SKUs produce the transactions that justify the investment.

When organizations prioritize their fastest-moving inventory for AS/RS storage, they maximize:

Labor savings

Pick productivity

Throughput performance

Equipment utilization

Return on investment

This is why leading warehouse operators often use ABC inventory analysis to determine AS/RS eligibility, with A-items receiving priority placement within the automated system.

The Hidden Cost of Selecting the Wrong SKUs

Many automation projects encounter challenges not because the technology underperforms, but because the SKU strategy was flawed from the start.

A common mistake is attempting to place too many slow-moving products into the AS/RS simply because capacity is available. While this may increase storage utilization, it often reduces economic utilization.

An AS/RS filled with low-velocity inventory can create several problems:

Lower Labor Savings

Products that rarely move generate few picking events. As a result, automation replaces little labor activity, weakening the original business case.

Reduced Throughput Benefits

System resources become occupied storing inventory that contributes minimally to order fulfillment activity, limiting the throughput impact of the investment.

Longer Payback Periods

If labor savings and productivity gains fall below projections, the expected payback period can extend significantly beyond the original ROI model.

Capacity Constraints

As demand grows, operators may discover that high-velocity items no longer have sufficient space within the AS/RS because slower-moving products are consuming capacity.

The technology itself may be performing exactly as designed, but the financial return can still disappoint.

Focus on Activity, Not SKU Count

One of the most important lessons for warehouse leaders is that AS/RS success should not be measured by the number of SKUs stored within the system.

Instead, it should be measured by the percentage of warehouse activity flowing through the system.

For example, placing 1,000 high-velocity SKUs into an AS/RS that captures 80% of total picking activity will typically generate greater value than storing 5,000 SKUs that account for only 40% of picks.

The goal is not to automate the greatest amount of inventory.

The goal is to automate the greatest amount of work.

That distinction has significant implications for system utilization and overall project economics.

The Importance of Continuous SKU Evaluation

SKU selection should not be treated as a one-time exercise conducted during system commissioning.

Demand patterns change. Product popularity shifts. New items are introduced while others decline.

As a result, warehouse operators should periodically re-evaluate which products belong inside the AS/RS. High-velocity inventory today may not be high-velocity inventory next year.

Organizations that continuously align automated storage capacity with current demand patterns are often able to sustain higher utilization rates and stronger ROI throughout the life of the system.

ROI strategy

Consider a simple ROI example. Assume a company invests $5 million in an AS/RS and expects to save $1 million annually through labor reduction and productivity improvements. The original business case assumes the system will handle 75 percent of warehouse picking activity.

If the company correctly slots high-velocity products into the system, the projected labor savings can be achieved, resulting in a payback period of approximately five years. However, if management fills a large portion of the AS/RS with slow-moving inventory, the system may only process 40 percent of warehouse picking activity. Annual savings could fall to $600,000, extending the payback period from 5 years to more than 8 years. The technology has not failed. The SKU strategy has.

Final Thoughts

AS/RS technology continues to play an increasingly important role in warehouse automation strategies. Yet successful implementations depend on much more than selecting the right hardware or software platform.

The most effective AS/RS deployments are built around careful SKU selection.

High-velocity products generate the majority of warehouse activity and therefore create the greatest opportunity for labor reduction, throughput improvement, and productivity gains. Conversely, filling automated systems with low-velocity inventory can significantly dilute returns and extend payback periods.

For warehouse and supply chain executives, the takeaway is straightforward: an AS/RS should be designed around activity, not occupancy. The objective is not simply to store products in an automated system. The objective is to automate the work that drives operational performance. In most facilities, that means ensuring the highest-velocity SKUs receive priority access to valuable AS/RS capacity.

The difference between a successful automation investment and an underperforming one may ultimately come down to a decision that appears deceptively simple: choosing the right SKUs.

The post Why SKU Selection Can Make or Break an AS/RS Business Case appeared first on Logistics Viewpoints.

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What the Descartes Innovation Forum Said About the Next Phase of Logistics Execution

Published

12 heures ago

13 octobre 2026

After several days at the 2026 Descartes Innovation Forum, the clearest takeaway was not simply that artificial intelligence is coming to logistics. That part is already obvious. What stood out more was where the technology is moving and why it matters now.

Across the keynote, a transportation leadership panel, a Walmart Marketplace session, and a detailed discussion of carrier fraud, the same operating problem kept surfacing in different forms. Logistics organizations have more data, more applications, more connectivity, and increasingly more AI, but the real value comes when those capabilities help someone make a better decision or execute the next step faster, more reliably, and with the right controls in place.

That matters because the operating environment is not getting easier. Transportation capacity is tightening, rates and fuel costs remain under pressure, fraud and cargo theft are serious concerns, customer expectations continue to rise, and companies are being asked to improve productivity without adding equivalent labor. Against that backdrop, the most interesting technology at the Forum was not technology that simply explained what was happening. It was technology aimed directly at the work itself.

The Swivel-Chair Problem Is Still Real

Descartes opened the Forum with a problem that will sound familiar to anyone who has spent time inside transportation operations. Companies may have capable systems and extensive networks, yet much of the actual work still happens in the gaps between those systems.

Someone receives an email, opens a portal, checks a transportation system, moves to another application, sends a message, waits for a response, updates a record, and then starts the process again somewhere else. Descartes referred to this as the “swivel chair tax,” and the phrase worked because it described a problem technology has never fully eliminated.

The company’s answer is its Agent Control Plane, which is designed to expose capabilities from Descartes applications as skills that AI agents can use. As I covered earlier in Descartes Takes Agentic AI Into the Logistics Workflow at the Descartes Innovation Forum, Descartes was careful to distinguish that approach from a basic chat interface or a traditional workflow engine. The intent is to allow agents to work across applications and adapt as conditions change rather than simply follow a rigid sequence of predefined screens.

The live demonstration made the concept more tangible. Descartes followed an order across four representative companies—a retailer, freight forwarder, customs broker, and final-mile provider—with 15 Descartes products supporting 28 process steps behind the Agent Control Plane. Many of those steps happened without requiring a person to manually move information from one application to another.

What stood out to me was not the amount of automation. It was where Descartes chose not to automate.

At one point, an agent identified a carrier option that could save roughly 8 percent. The carrier, however, had a poor trust score, thin operating history, and other warning signs. Rather than make the lower-cost choice automatically, the system surfaced the decision for human review. The human declined the cheaper carrier.

That is a much more practical model for agentic AI in logistics than the idea of simply handing an operating process over to a machine. Descartes repeatedly described autonomy as a dial, not a switch. Agents have identities, their actions are logged, activity can be replayed, and organizations can determine which decisions require human approval.

The significance is straightforward. Once AI begins taking actions inside real operating processes, governance stops being an abstract IT issue and becomes part of the operating model.

The Market Is Making Better Execution More Valuable

The transportation leadership panel I moderated later in the Forum put that technology discussion into a much tougher operating context.

The freight market described by the panelists was not one of strong demand and easy growth. It was one in which relatively weak demand is colliding with meaningful reductions in capacity. One participant cited a decline in the number of trucking companies from roughly 340,000 to 282,000 and dry-van truckload rates up approximately 33 percent year over year. The larger point was that the conversation has shifted. Two years ago, shippers were largely focused on rates. Today, capacity availability and reliability matter much more.

That tighter environment also increases risk. When capacity is harder to find, there is greater temptation to use carriers that may not have passed the same diligence in a looser market. Panelists tied that pressure to growing concerns around freight fraud, cargo theft, safety, insurance, litigation, and carrier qualification.

As discussed in Transportation Leaders See AI Moving From Experiment to Operating Model at the Descartes Innovation Forum, the response is not simply to cut spending. Technology did not sound discretionary in this discussion. One participant described a single repetitive business activity being performed roughly 750,000 times per month, representing the equivalent of about 1,500 employee-hours per day. That is exactly the kind of workload where automation can move from being interesting to economically significant.

The panel also showed how quickly expectations around technology development are changing. Voice agents, email automation, operational workflows, data science, and agentic AI were all discussed as practical tools rather than distant possibilities. One participant described a proprietary TMS containing more than 30 million lines of code and roughly 2,000 database tables. A 12-person team working with AWS and Anthropic was then tasked with recreating the system using AI-assisted development and reportedly produced a replication in approximately six weeks.

Replicating a system is not the same as proving it in full production, so that example deserves some caution. Still, it shows how quickly the economics of software development may be changing.

Transportation technology has traditionally changed slowly because systems were complex, deeply integrated, and expensive to modify. AI may compress that cycle dramatically. For transportation leaders, that creates an interesting tension: the operating environment is getting harder at the same time that the technology available to respond to it is improving faster.

Walmart Brought the Conversation Back to the Customer

A Walmart Marketplace session provided a useful counterweight to the AI-heavy discussions elsewhere at the Forum. Bryce Caswell, Director of Ship with Walmart for Walmart Marketplace, focused on something much more basic: the customer promise.

Walmart described four foundations for marketplace success—assortment, listing quality, pricing, and shipping—and emphasized that the four are interdependent. Good assortment will not solve poor content. Good content will not offset uncompetitive pricing. And none of it matters if the seller cannot deliver the product when promised.

That may sound obvious, but it was one of the more useful reminders of the Forum. Logistics technology discussions can quickly become dominated by architecture, optimization, visibility, and AI. The customer evaluates something much simpler: did the order arrive when you said it would?

As I wrote in Walmart Is Turning Marketplace Logistics Into a Platform, Walmart’s emphasis was not simply on speed. Reliability and consistency mattered just as much. During holiday peaks, sellers were encouraged to protect inventory availability, leave enough time to move product into the fulfillment network, understand what they are promising, and then meet that promise.

The company is also trying to simplify the mechanics underneath that promise. Walmart Fulfillment Services provides pick, pack, and ship capabilities through Walmart’s fulfillment network, while its multi-channel model allows sellers to support multiple sales channels from a single inventory pool.

Ship with Walmart extends that logic into transportation by offering access to negotiated carrier rates, including national, regional, and LTL options, while adding certain protections around on-time delivery and lost shipments.

The larger point is that simplification has value only if it improves execution. Whether the tool is an AI agent or a fulfillment network, the objective is the same: remove unnecessary friction so people can focus on the decisions and exceptions that actually matter.

Fraud Changes the Meaning of Visibility

The Forum’s carrier fraud session added another layer to the discussion because it showed how operating data is being repurposed for trust.

Descartes demonstrated how MyCarrierPortal and MacroPoint can be used together to strengthen carrier vetting before a load is tendered. The process extends beyond checking insurance or basic carrier status. It can incorporate configurable risk assessments, tracking history, lane history, inspection information, validated users, equipment data, and driver-level tracking statistics.

That is a subtle but important shift in how transportation visibility data is used. Historically, tracking information primarily answered a familiar question: where is my load? Increasingly, that same information can help answer a different question: should I trust the party moving it?

The same theme runs through Descartes Expands Freight Fraud Prevention Across Carrier Vetting and Shipment Execution. Descartes showed how historical lane activity and tracking behavior can be used as additional carrier-vetting signals. Once a shipment is in transit, the Notification Center can surface route deviations and other exceptions that warrant attention without requiring someone to monitor every load manually.

ELD Plus adds another layer by allowing two tracking sources to be used within the same tracking session. If mobile-device data and telematics data begin to diverge, that mismatch becomes a signal that someone may need to investigate.

Carrier lookup goes further by combining information such as power units, active loads, tracking history, spoofing flags, and recent lane activity. The example used during the session was simple: if a carrier has 100 power units but appears to be active on 600 loads, that discrepancy may deserve a closer look.

More automation does not reduce the need for trust. It raises the stakes. If systems are going to make recommendations or take actions faster, the quality of the signals behind those decisions becomes even more important.

Execution Is Becoming the Real Battleground

Looking back across the Forum, I do not think the central story was simply AI. It was execution.

Descartes is trying to reduce the swivel-chair work that still exists between logistics applications and companies. Transportation leaders are looking for technology that can improve productivity and help them operate through a tighter, riskier freight market. Walmart is focused on simplifying fulfillment so sellers can keep the customer promise. Carrier-verification tools are turning tracking data into a source of trust as well as visibility.

Put together, that points to a broader shift in logistics technology. Software is moving closer to the actual transaction, the actual decision, and the actual exception. The value of that technology will increasingly be judged by whether it helps organizations act better, not simply see more.

The companies that benefit most will not necessarily be the ones with the most AI, the most dashboards, or the most data. They will be the ones that can connect intelligence to execution without losing control of the process.

For me, that was the real takeaway from the 2026 Descartes Innovation Forum. The next phase of logistics technology is not just about making systems smarter. It is about knowing when the system should act, when a person should decide, and how to make both work together fast enough to keep freight moving and the customer promise intact.

The post What the Descartes Innovation Forum Said About the Next Phase of Logistics Execution appeared first on Logistics Viewpoints.